Twelve tips for developing and implementing AI curriculum for undergraduate medical education

Abstract

The rapid evolution of artificial intelligence (AI) and its growing role in clinical settings have made AI education a priority in undergraduate medical education. To support this, AI curricula must align with existing medical education frameworks while addressing AI's distinctive characteristics. This article outlines twelve actionable tips to guide the development and implementation of such curricula. These include defining the purpose and scope of AI education within the broader context of existing competency frameworks and digital health. The curriculum should be structured to allow for progressive deepening and integration of content, prioritizing key elements. Additionally, sustainable AI education depends on securing institutional resources, providing learners with authentic experiences, and ensuring continuous evaluation and improvement of the curriculum. Together, these approaches aim to help medical schools prepare students to practice effectively in a future where AI is a core component of medical practice.

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